Abstract

Metabolite exchanges in microbial communities give rise to ecological interaction networks that influence ecosystem diversity and stability. These exchanges depend on complex intracellular pathways thus raising the question of whether ecological interactions are inferable from genomes. We address these questions by integrating genome-scale models of metabolism, to compute the fitness of interacting microbes, with evolutionary game theory, which uses these fitness values to infer evolutionarily stable interactions in multi-species microbial "games". After validating our approach using data on sucrose hydrolysis by S. cerevisiae, we performed over 80,000 in silico experiments to evaluate the rise of unidirectional and cross-feeding metabolic dependencies in populations of Escherichia coli secreting 189 amino acid pairs. We found that, despite the diversity of exchanged amino acids, most pairs conform to general patterns of inter-species interactions. However, several amino acid pairs deviate from these patterns due to pleiotropy and epistasis in metabolic pathways. To better understand the emergence of cross-feeding, we performed in silico invasion experiments and found possible evolutionary paths that could lead to such association. Overall, our study provides mechanistic insights into the rise of evolutionarily stable interdependencies, with important implications for biomedicine and microbiome engineering.